

import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowercase			:				List[str]   =     version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
		from nltk import word_tokenize


lowercase			:				Union[str, Any]   =     "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowercase			:				List[str]   =     "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowercase			:				List[Any]   =     "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,       _KWARGS_DESCRIPTION				)
class 					__lowercase  (						datasets.Metric				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       str:
														return datasets.MetricInfo(
														    description=_DESCRIPTION    ,						citation=_CITATION    ,						inputs_description=_KWARGS_DESCRIPTION    ,						features=datasets.Features(
														        {
														            '''predictions''': datasets.Value('''string'''    ,						id='''sequence'''						),
														            '''references''': datasets.Value('''string'''    ,						id='''sequence'''						),
														        }						)    ,						codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py''']    ,						reference_urls=[
														        '''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
														        '''https://en.wikipedia.org/wiki/METEOR''',
														    ]    ,						)




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       str:
														import nltk

														nltk.download('''wordnet'''						)
														if NLTK_VERSION >= version.Version('''3.6.5'''						):
																					nltk.download('''punkt'''						)
														if NLTK_VERSION >= version.Version('''3.6.6'''						):
																					nltk.download('''omw-1.4'''						)






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase=0.9    ,						__UpperCAmelCase=3    ,						__UpperCAmelCase=0.5						)		->       Tuple:
														if NLTK_VERSION >= version.Version('''3.6.5'''						):
																					A      :					Union[str, Any]      		=					[
																					    meteor_score.single_meteor_score(
																					        word_tokenize(__UpperCAmelCase						)    ,						word_tokenize(__UpperCAmelCase						)    ,						alpha=__UpperCAmelCase    ,						beta=__UpperCAmelCase    ,						gamma=__UpperCAmelCase						)
																					    for ref, pred in zip(__UpperCAmelCase    ,						__UpperCAmelCase						)
																					]
														else:
																					A      :					Tuple      		=					[
																					    meteor_score.single_meteor_score(__UpperCAmelCase    ,						__UpperCAmelCase    ,						alpha=__UpperCAmelCase    ,						beta=__UpperCAmelCase    ,						gamma=__UpperCAmelCase						)
																					    for ref, pred in zip(__UpperCAmelCase    ,						__UpperCAmelCase						)
																					]

														return {"meteor": np.mean(__UpperCAmelCase						)}









import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
		import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin







@require_torch
class 					__lowercase  (						unittest.TestCase		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					load_tool('''text-to-speech'''						)
														self.tool.setup()




							def       snake_case			(					self						)		->       Union[str, Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					Optional[Any]      		=					self.tool('''hey'''						)
														A      :					List[str]      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






							def       snake_case			(					self						)		->       Optional[Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					int      		=					self.tool('''hey'''						)
														A      :					Any      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






